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Veri.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:805efaf38201cbb3716af3195913dd1cc6e1f5dd04859e53f2ae115b0c15f8b4
+size 8980

app.py

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+import pandas as pd
+import streamlit as st
+import joblib
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+
+# Load data and update column names
+df = pd.read_csv('dataset.csv')
+df = df.drop(['id','Class'], axis=1)
+df.columns = df.columns.str.replace(r'[\s\.]', '_', regex=True)
+df.columns = df.columns.str.replace(r'Gender&Type', 'Gender_Type', regex=True)
+df.columns = df.columns.str.replace(r'#', '', regex=True)
+
+df[['Monthly_Period', 'Credit1', 'InstallmentRate', 'Tenancy_Period', 'Age', 'Credits', 'Authorities']] = df[['Monthly_Period', 'Credit1', 'InstallmentRate', 'Tenancy_Period', 'Age', 'Credits', 'Authorities']].apply(pd.to_numeric, errors='coerce').fillna(0).astype(int)
+df[['InstallmentCredit', 'Yearly_Period']] = df[['InstallmentCredit', 'Yearly_Period']].apply(pd.to_numeric, errors='coerce').fillna(0).astype(float)
+
+# Select dependent and independent variables
+x = df
+
+# Preprocessing (StandardScaler)
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ["Monthly_Period", "Credit1", "InstallmentRate", "Tenancy_Period", "Age", "Credits", "Authorities", "InstallmentCredit", "Yearly_Period"]),
+        ('cat', OneHotEncoder(), ["Account1", "History", "Motive", "Account2", "Employment_Period", "Gender_Type", "Sponsors", "Plotsize", "Plan", "Housing", "Post", "Phone", "Expatriate"])
+    ]
+)
+
+# Streamlit application
+def cluster_pred(Account1, Monthly_Period, History, Motive, Credit1, Account2, Employment_Period, InstallmentRate, Gender_Type, Sponsors, Tenancy_Period,
+                 Plotsize, Age, Plan, Housing, Credits, Post, Authorities, Phone, Expatriate, InstallmentCredit, Yearly_Period):
+    input_data = pd.DataFrame({
+        'Account1': [Account1],
+        'Monthly_Period': [Monthly_Period],
+        'History': [History],
+        'Motive': [Motive],
+        'Credit1': [Credit1],
+        'Account2': [Account2],
+        'Employment_Period': [Employment_Period],
+        'InstallmentRate': [InstallmentRate],
+        'Gender_Type': [Gender_Type],
+        'Sponsors': [Sponsors],
+        'Tenancy_Period': [Tenancy_Period],
+        'Plotsize': [Plotsize],
+        'Age': [Age],
+        'Plan': [Plan],
+        'Housing': [Housing],
+        'Credits': [Credits],
+        'Post': [Post],
+        'Authorities': [Authorities],
+        'Phone': [Phone],
+        'Expatriate': [Expatriate],
+        'InstallmentCredit': [InstallmentCredit],
+        'Yearly_Period': [Yearly_Period]
+    })
+
+    model = joblib.load('Veri.pkl')
+    scaler = StandardScaler()
+
+    input_data_transformed = pd.get_dummies(input_data, drop_first=True)
+    input_data_transformed = input_data_transformed.reindex(columns=x.columns, fill_value=0)
+    input_data_transformed = scaler.transform(input_data_transformed)
+
+
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    
+
+    prediction = model.predict(input_data_transformed)
+    return float(prediction[0])
+
+st.title("KMeans Clustering Model")
+st.write("Enter Input Data to Predict Cluster")
+
+Account1 = st.selectbox('Account1', df['Account1'].unique())
+Monthly_Period = st.slider('Monthly_Period', int(df['Monthly_Period'].min()), int(df['Monthly_Period'].max()))
+History = st.selectbox('History', df['History'].unique())
+Motive = st.selectbox('Motive', df['Motive'].unique())
+Credit1 = st.slider('Credit1', int(df['Credit1'].min()), int(df['Credit1'].max()))
+Account2 = st.selectbox('Account2', df['Account2'].unique())
+Employment_Period = st.selectbox('Employment_Period', df['Employment_Period'].unique())
+InstallmentRate = st.slider('InstallmentRate', int(df['InstallmentRate'].min()), int(df['InstallmentRate'].max()))
+Gender_Type = st.selectbox('Gender_Type', df['Gender_Type'].unique())
+Sponsors = st.selectbox('Sponsors', df['Sponsors'].unique())
+Tenancy_Period = st.slider('Tenancy_Period', int(df['Tenancy_Period'].min()), int(df['Tenancy_Period'].max()))
+Plotsize = st.selectbox('Plotsize', df['Plotsize'].unique())
+Age = st.slider('Age', int(df['Age'].min()), int(df['Age'].max()))
+Plan = st.selectbox('Plan', df['Plan'].unique())
+Housing = st.selectbox('Housing', df['Housing'].unique())
+Credits = st.slider('Credits', float(df['Credits'].min()), float(df['Credits'].max()))
+Post = st.selectbox('Post', df['Post'].unique())
+Authorities = st.slider('Authorities', float(df['Authorities'].min()), float(df['Authorities'].max()))
+Phone = st.selectbox('Phone', df['Phone'].unique())
+Expatriate = st.selectbox('Expatriate', [True, False])
+InstallmentCredit = st.slider('InstallmentCredit', float(df['InstallmentCredit'].min()), float(df['InstallmentCredit'].max()))
+Yearly_Period = st.slider('Yearly_Period', float(df['Yearly_Period'].min()), float(df['Yearly_Period'].max()))
+
+if st.button('Predict Cluster'):
+    cluster = cluster_pred(Account1, Monthly_Period, History, Motive, Credit1, Account2, Employment_Period, InstallmentRate, Gender_Type, Sponsors, Tenancy_Period,
+                            Plotsize, Age, Plan, Housing, Credits, Post, Authorities, Phone, Expatriate, InstallmentCredit, Yearly_Period)
+    st.write(f'The predicted cluster is: {cluster}')

encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2003d9a0f3831c59a10a17d937a1eb114d2a78ccab82d964ffe4e0ff52499d2
+size 271

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c85f548747e9516323485c728c301bebac9fbdde5309a017de33739ddb6514ab
+size 129